Quick Navigation
Topics
Trapped Ion Quantum Computing
Quantum Machine Learning
Quantum Computation with Machine-Learning-Controlled Quantum Stuff
arXiv
Authors: Lucien Hardy, Adam G. M. Lewis
Year
2019
Paper ID
14431
Status
Preprint
Abstract Read
~2 min
Abstract Words
95
Citations
N/A
Abstract
We describe how one may go about performing quantum computation with arbitrary "quantum stuff", as long as it has some basic physical properties. Imagine a long strip of stuff, equipped with regularly spaced wires to provide input settings and to read off outcomes. After showing how the corresponding map from settings to outcomes can be construed as a quantum circuit, we provide a machine learning algorithm to tomographically "learn" which settings implement the members of a universal gate set. At optimum, arbitrary quantum gates, and thus arbitrary quantum programs, can be implemented using the stuff.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2019 reference point for readers tracking recent quantum research.
- We describe how one may go about performing quantum computation with arbitrary "quantum stuff", as long as it has some basic physical properties.
Paper Tools
Become a member to use research tools
Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.
Show Paper arXiv Publisher Share
Cite This Paper
Copy URL
Compare
Copy DOI Add to Reading List
Category Correction Request
Category Correction Request
Help us improve classification quality by proposing a better category. Every request is reviewed by an admin.
Sign in to submit a category correction request for this paper.
Log In to SubmitReferences & Citation Signals
Community Reactions
Quick sentiment from readers on this paper.
Score:
0
Likes: 0
Dislikes: 0
Sign in to react to this paper.
Discussion & Reviews (Moderated)
Average Rating: 0.0 / 5 (0 ratings)
No written reviews yet.